Winter 2027: Sessional Lecturer: LMP1210H: Basic principles machine learning biomedical research

University of Toronto; Toronto; On-site; CAD 9,000 - 14,000; Part time

Job summary

The University of Toronto invites applications for Winter 2027 as a Sessional Lecturer for the LMP1210H course on basic principles of machine learning in biomedical research, hosted by the Temerty Faculty of Medicine. The position is 0.25 FCE, co-taught, with responsibilities spanning lectures, seminars, and assessment.

Required is a PhD in computer science or a health science field with ML expertise and Python programming. Strong leadership in curating technical material is essential.

Qualifications

• PhD in computer science or health science with a strong background in machine learning and its biomedical applications.
• Programming skills in Python are required for the assignments. Demonstrated leadership in curating technical material for audiences.

Responsibilities

• Preparation and delivery of course content, including lectures and seminars; creation of courseware; development and administration of assignments, tests and examinations; marking and grading of student work and submission of grades.
• Creation, maintenance and management of the Quercus site for the course, collecting material from lecturers in advance and posting slides, handouts, readings and bibliographies.

Skills

Python

Education

PhD in computer science or health science with ML

Job description

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Winter 2027: Sessional Lecturer: LMP1210H: Basic principles machine learning biomedical research

Date Posted:10/24/2026
Closing Date: 11/13/2026 , 11:59PM EDT
Req ID: 50753
Job Category:Sessional Instruction
Faculty/Division:Temerty Faculty of Medicine
Department:Dept of Lab. Medicine & Pathobiology
Campus:St. George (Downtown Toronto)

Description:

This course is intended for graduate students in Health Sciences to learn the basic principles of machine learning in biomedical research and to build and strengthen their computational skills of medical research. The course aims to equip students with the fundamental knowledge of machine learning (ML).
During the course, students will acquire basic computational skills and hands-on experience to deploy ML algorithms using python. Students are expected to be familiar in python prior to taking this course.
Students will learn the current practices and applications of ML in medicine and understand what ML can and cannot do for medicine. The goal of this course to establish an essential foundation for graduate students to take the first steps in computational research in medicine.

Number of available positions: 1 postion x 0.25 FCE (co-teaching course)

Estimated TA support: not applicable

Class schedule: Wednesays from 11:00 AM – 1:00 PM

Sessional dates of appointment: January 1, 2027 – April 30, 2027

Salary (0.25 FCE - course will be co-taught):

Please note that should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.

Minimum qualifications: PhD in computer science or in a health science discipline with a strong background in machine learning and its application in biomedical research. Programming skills in Python are required for the assignments. Also required is the demonstration of leadership in curating technical material to present to an audience as demonstrated by the organization of research workshops.

Preferred qualifications: Extensive research experience with published research papers applying ML in biomedical research is strongly preferred. Experience creating and maintaining a website that highlights the course requirements, announcements and grade breakdown

Description of duties: Preparation and delivery of course content, including lectures and/or seminars; creation of courseware; the development and administration of assignments, tests and examinations; marking and grading of student work and the submission of grades to appropriate University officials, maintenance of reasonable hours of student contact outside of scheduled contact hours. Creation, maintenance and management of the Quercus site for the course, collecting material from the lecturers in advance and posting lecturer slide sets (power points), handouts, reading lists, journal articles and bibliographies and preparing other course content as required.

Diversity Statement

The University of Toronto embraces Diversity and is building aculture of belonging that increases our capacity to effectivelyaddress and serve the interests of our global community. Westrongly encourage applications from Indigenous Peoples,Black and racialized persons, women, persons withdisabilities, and people of diverse sexual and gender identities.We value applicants who have demonstrated a commitment toequity, diversity and inclusion and recognize that diverseperspectives, experiences, and expertise are essential tostrengthening our academic mission.
As part of your application, you will be asked to complete a brief Diversity Survey. This survey is voluntary. Any information directly related to you is confidential and cannot be accessed by search committees or human resources staff. Results will be aggregated for institutional planning purposes. For more information, please see http://uoft.me/UP .

Accessibility Statement

The University strives to be an equitable and inclusive community, and proactively seeks to increase diversity among its community members. Our values regarding equity and diversity are linked with our unwavering commitment to excellence in the pursuit of our academic mission.
The University is committed to the principles of the Accessibility for Ontarians with Disabilities Act (AODA). As such, we strive to make our recruitment, assessment and selection processes as accessible as possible and provide accommodations as required for applicants with disabilities.
If you require any accommodations at any point during the application and hiring process, please contact [e-mail] .

The University of Toronto has implemented a process to substantiate Indigenous Membership/Citizenship/Enrolment for candidates who identify as Indigenous. For more information about this process, please refer to the Indigenous Substantiation Website .

This job is posted in accordance with the CUPE 3902 Unit 3 Collective Agreement.

It is understood that some announcements of vacancies are tentative, pending final course determinations and enrolment. Should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.

Preference in hiring is given to qualified individuals advanced to the rank of Sessional Lecturer II or Sessional Lecturer III in accordance with Article 14:12 of the CUPE 3902 Unit 3 collective agreement.

Please note: Undergraduate or graduate students and postdoctoral fellows of the University of Toronto are covered by the CUPE 3902 Unit 1 collective agreement rather than the Unit 3 collective agreement, and should not apply for positions posted under the Unit 3 collective agreement.